sqlmodel VS typer

Seems like this is a known bug in SQLModel: https://github.com/tiangolo/sqlmodel/issues/10

I have not used it myself but the creator of fastapi has made https://github.com/tiangolo/sqlmodel

I have already posted this question in Stack Overflow and GitHub and have been ignored in both 😢. You guys are my last hope.

I had a requirement to process and validate large payloads of JSON concurrently for a web service, initially I implemented it using jsonschema and fastjsonschema but I found the whole JSON Schema Specification to be confusing at times and on top of that wanted better performance. Albeit there are ways to compile/cache the schema, I wanted to move away from the schema specification so I wrote a validation library inspired by the design of tiangolo/sqlmodel (type hints) to solve this problem easier.

Github Repository

I would say as long as your current solution works and is easy to maintain keep it. If you want to switch I would recommend FastAPI, it is new(ish), but definitely old enough to have been tested and used in a large variety of production usecases. In your case it might be interesting to have a look at SQLModel (works with FastAPI, same author), especially if the API endpoints match closely to the database objects. https://github.com/tiangolo/sqlmodel

SQLModel.

Sounds similar to https://github.com/tiangolo/sqlmodel

There was a lot of hype and excitement around the release of SQLModel, a Pydantic + SQLAlchemy hybrid Model library with native integration for FastAPI. I pulled it out just now and there hasn't been any update beyond the initial anouncment on August 25th.

I have been using Typer on every one of my CLI projects which uses Click under the hood. The documentation is fantastic, the CLI app it produces looks great and lets you create things quickly. I high recommend it.

https://typer.tiangolo.com/

Adding CLI capabilities. My preferred library here is typer.

I just heard about this but it seems to be pretty much the type of thing you want and want fast.

Docstrings are for documenting how a function/ class/ method/ module works. Often you don't need to add a docstring to your main function because no one will be importing it to use elsewhere. And if you want it to run as a CLI, then there are better ways to document the available options. For example, typer does most of it for you, or in click you add the help text to the decorator.

Most computing tasks in airflow DAGs are KubernetesPodOperator containing a CLI (Python Typer). It allows us to pass arguments easily to run DAG manually if needed (the new UI to pass arguments to DAG in airflow 2.6 is really nice). Arguments allow us to replay DAG easily (change start / end dates for instance).

So it's basically the main .gitlab-ci.yml file plus a separate Python CI app using Typer for the AWS instrumentation.

Similarly for Typer, which is literally "the FastAPI of CLIs"[1]. Handy to type your `main` parameters and have CLI argument parsing. For more complicated cases, it's a wrapper around Click.

[1] https://typer.tiangolo.com/

interesting that you hate python, but love Click. Did you try Typer which uses Click underneath?